Книги по МРТ КТ на английском языке / Functional Neuroimaging in Child Psychiatry Ernst 1 ed 2000

the brain. Multiple brain regions are most likely involved for even the simplest cognitive processes. If these regions are sequentially activated, this may pose no particular problem for the standard moving dipole model discussed above, since a single ECD can be Wt across a time epoch point by point. However, to the extent that the multiple sources overlap temporally, the single ECD model will tend to produce more serious errors. Even if the sources do overlap in time, however, a single ECD model may suYce if the sources are separated by a large enough distance in space (this minimum distance is empirically unknown and depends on the sensor array density, geometry of the sources and detectors, as well as the SNR for the sources). For example, auditory stimuli, even when monaurally pre-

sented, produce near simultaneous activation in both temporal lobes. However, the magnetic Weld distributions from the two hemispheres do not overlap, and a single ECD can be used for each hemisphere to produce satisfactory results. If the sources overlap in time and space, a multiple dipole model may need to be employed. The spatiotemporal dipole model is one such approach that has been adapted to include multiple dipole modeling (e. g. Scherg et al., 1989).

Several other variations of the source analysis scheme have been employed in MEG, such as distributed source modeling, but are well beyond the scope of this chapter because of their complexity. The reader is referred to Sarvas (1987), Hamalainen et al. (1993), Williamson and Kaufman (1990), and Romani (1987) for further information on MEG

and source analysis and to Mosher et al. (1992) for an overview of multiple source modeling in MEG.

Magnetic source imaging

One often wants to know not only the three-dimensional coordinates of the current source underlying the magnetic Weld distribution but also where that point is in relation to the anatomy of the brain. Therefore, source locations are

often coregistered onto anatomically detailed MRI from the same subject. This is accomplished by transforming the coordinate system from MEG into the MRI system, or vice versa, a procedure that is eloquently described by Rezai et al. (1995). The combination of information from MEG source analysis with MRI data is often referred to as magnetic source imaging (Gallen et al., 1993, 1995; Chuang et al., 1995; Lewine and Orrison, 1995b). Figure 5.14 illus-

Fig. 5.10. The Neuromag, Ltd Vectorview whole-head system with child subject in special chair insert. This system, like the one shown in Fig. 5.9, is capable of recording in seated and fully inclined positions. The Vectorview system employs 204 oV- diagonal planar gradiometers (see Fig. 5.5.d) and 102 magnetometers (Fig. 5.5a) in 100 sensor locations (i.e., three orthogonal channels per location). (Courtesy of Neuromag, Ltd.)

trates a typical magnetic source imaging result for MEG data obtained from auditory evoked Weld (AEF) study.

Comparison of magnetoand electroencephalography

DiVerences in recorded signal

MEG is related to EEG in a complex way. The most important diVerence between the two techniques is the measured signal from neuronal activity: EEG reXects primarily extracellular current and MEG measures intracellular current. The origin of the magnetic signals of the brain was discussed above. EEG signals are also generated by the

Fig. 5.11. The eVect of signal averaging on the auditory evoked

Weld. The topmost waveform is a two-trial averaged evoked response from a gradiometer channel located over the left anterior magnetic extremum of the M100 evoked Weld. From top to bottom, the rest of the waveforms are 4, 6, 8, 16, 32, and 64 trial averages from the same data set. Note the reduction in amplitude of the prestimulus baseline with the inclusion of more trials in the average, and the approximately 100ms latency auditory evoked response in the poststimulus window.

same ionic events that produce magnetic Welds. As an example, the major contribution to depolarizing currents at the synapse is a brief increase in sodium ion permeability, typically induced by ligand gating of a protein ion channel on the postsynaptic membrane. This allows sodium ions to move freely along their chemical and electric gradients, usually producing a net inXow of sodium ions into the cell (transmembrane current). This local accumulation in turn causes intracellular current Xow away from the open ion channels, causing an accumulation of positively charged ions some small distance away from the original inXow and triggering an outward transmembrane current Xow. Since positive ions are now accumulating locally just outside the outward Xow (called a current source), displacement of like charges occurs just as it did inside the cell. This time, the extracellular current will be drawn back to the original inXow because a local extracellular depletion of positive charge (called the current sink) will be present there. The extracellular component of this event is commonly referred to as volume conduction and is largely responsible for the observed potential distribution seen at the scalp in EEG. Since the entire brain and

to some extent the meninges, cerebrospinal Xuid, and scalp are all reasonable current conductors, strong volume currents may be seen quite some distance from the neuronal activity that produced them. This contrasts directly with MEG, which because it is primarily measuring the magnetic Weld produced by the intracellular current, is likely to see a more focal Weld distribution. In addition, the skull, a relatively nonconductive medium, can seriously attenuate and distort the electric potential distribution (seen in EEG) of neuronal sources, whereas the magnetic Welds of the brain (as in MEG) pass through the skull with no distortion (van den Broek et al., 1998). These two factors

are, at least in theory, likely to confer a slight spatial resolution advantage to MEG over EEG (see below).

Another key diVerence between MEG and EEG in the recorded signal is that, although MEG is highly sensitive to source orientation since radially oriented sources produce no external magnetic Weld (Fig. 5.2, p. 82), both radial and tangential sources contribute to the scalp EEG (Fig. 5.15, p. 82). This can be seen both as an advantage and a disadvantage for EEG: advantage because there is more cortex visible to EEG, and disadvantage because of the resulting increase in complexity of sources contributing to the potential distribution at the scalp, which in turn requires

more complex source models and increasing ambiguities about model accuracy.While it seems readily apparent that combining the two technologies could yield richer interpretation of the data than either alone can, the two techniques have rarely been combined in any systematic way (see, however, Diekmann et al., 1998).

Finally, there is always the problem of reference in EEG measurements. The potential measurements made in EEG are, by deWnition, diVerence measurements and require the use of two electrode inputs into each ampliWer channel. Since the diVerence (potential) between the two electrode sites is what is recorded in EEG, the locations of and distances between the paired electrodes are critical for interpreting the nature of the recorded signal. This problem has been discussed in detail elsewhere (Lewine and Orrison, 1995a) and will not be elaborated here. SuYce to say that MEG measurements are truly reference-free, and, therefore, signals detected by a single coil can be more easily interpreted with respect to their origin. EEG researchers are, however, able to reduce the impact of the reference problem, usually by computing an average potential reference from all the recording electrodes (which requires a large number of electrodes for accuracy) or by computing the second spatial derivative of the potential distribution (the Laplacian), which is reference-free.

Potential for source localization

There are few direct empirical comparisons of the localization capability of MEG and EEG that can be considered fair. A legitimate direct comparison would have to be for a known source with a tangential orientation, since the point is moot for radial sources in MEG (EEG will always be superior in that case). In addition, a fair comparison would need to have comparable intersensor distances and adequate coverage of the magnetic and electric topographies, both of which are acquired simultaneously with identical stimulation/recording parameters. Only three studies published to date appear to fulWll most of these criteria (with the exception of simultaneity in the Wrst case). Cohen et al. (1990) studied an implanted dipole source in a surgical patient, measuring the activation of this dipole from 16 diVerent sites with both EEG and MEG. The average localization errors for diVerent conditions were 8mm for MEG and 10mm for EEG, and the authors suggested that MEG oVered no signiWcant advantage over EEG in localizing accuracy. The most recent comparison of MEG and EEG localization was not directly of accuracy; instead, localization replicability was compared in Wve subjects who underwent repeated high-density, simultaneous EEG/MEG

recording of auditory evoked responses (Virtanen et al., 1998). Localization errors reported over the repeated recordings were 2mm for MEG and 4mm for EEG (average standard deviation for x, y, and z coordinates). Computer simulations and theory oVer similar conclusions (Cohen and CuYn, 1991; Lopes da Silva et al., 1991; Mosher et al., 1993; Haueisen et al., 1997). However, MEG source modeling can usually be done satisfactorily with simpler head models, whereas accurate EEG source modeling may require more sophisticated head models that take head shape and conductivity layers into account (Lopes da Silva et al., 1991). It should also be mentioned that for some realworld applications of source localization, MEG may still oVer better spatial resolution, as may be the case for the localization of seizure foci (Nakasato et al., 1994). In any case, MEG may not have as great an advantage in localization accuracy over EEG for a single ECD as was once proposed (e.g., CuYn and Cohen, 1979). DiVerences might exist for more complex source geometries, but this will need to be empirically evaluated. As it currently stands, the main advantage of MEG may be its sensitivity to source orientation. The most signiWcant disadvantage of MEG compared with EEG remains its signiWcant cost (see Table 5.1 for comparison of MEG/EEG features).

Pediatric research applications

Auditory evoked magnetic Welds of the human fetus

One potential advantage of MEG over all other functional imaging modalities is its ability to record the brain's magnetic responses prenatally. Blum and colleagues (Blum et al., 1984, 1985, 1987) were the Wrst to report AEF from the fetus. In these studies, the position of the fetus in utero was determined through ultrasound imaging, and the ear canal of the fetus was projected onto the mother's abdomen, where the authors centered the MEG device, a singlechannel, second-order gradiometer. They delivered acoustic stimuli (1kHz sine waves) from 40 to 100dB HL (mother's threshold) via a speaker located 1m away from the mother, and averaged the responses from 300 stimuli to produce AEF waveforms. Two subjects were studied prenatally, and one was recorded again after birth. Both subjects showed a small (#200fT) AEF component at approximately 140ms poststimulus, which was also present in the single subject followed up after delivery. Polarity reversals in this component were seen from anterior to posterior across the ear canal, consistent with adult AEF data indicating an auditory cortical source. Wakai et al. (1996) have

Single largest operational cost (personnel costs and optional maintenance contracts excluded)

Time to prepare and record patientc (min)

Liquid helium: $4.00/l, 100l/week5 $20800/year (costs are typical for users in the USA; they may be substantially higher for other countries)

, 30

Replacement of electrode cap/net as elasticity degrades: estimated cost for 128-channel cap is $2000. Estimated need for replacement is every 100 patients

, 90

replicated these Wndings in recordings from 14 fetuses between 36 to 40 weeks gestational age (Fig. 5.16).

Auditory evoked magnetic Welds in children

Three studies have been published to date concerning AEF development in children. Paetau et al. (1995) were the Wrst to report AEF values in children between the ages of 4 months and 15 years (n5 23). Using pure tone and phonemic stimuli, they reported a lack of the M100 (100ms latency magnetic Weld; also termed N100m) AEF waveform in children up to 12 years of age. The M100 waveform, the magnetic analog of the electric vertex N100 auditory evoked

potential, is the most prominent AEF component (i.e., largest amplitude) seen in adult magnetic recordings. Paetau et al. (1995) also reported that for longer interstimulus intervals ($1s), the M100 component appeared in most of the younger children's recordings. The authors interpreted this as possible evidence of a refractory period change in the underlying neuronal population for the M100. In two separate studies conducted in our laboratory, this hypothesis was tested systematically by varying the interstimulus interval between pure tone stimuli (Rojas et al., 1998, 1999). In the Wrst study, six children aged between 8 and 9 years of age participated, and 22 children between 6 and 18 years of age participated in the second. Both studies

(a)

(b)

(c )

(d )

Fig. 5.16. Fetal MEG recording. Auditory evoked magnetic Welds AEF from four diVerent fetal subjects: (a) 37, (b) 34, (c) 35, and (d) 36 weeks gestational age. The placement of the MEG device was determined by ultrasound. Sounds were delivered via a loudspeaker mounted in the recording chamber. The AEF responses are averages of 100 epochs. Stimulus onset is at time zero. (With permission from Wakai et al., 1996.)

provided evidence supportive of the refractory period change hypothesis (Fig. 5.17). These studies, taken together, suggest that one reason the EEG evoked potential recordings in children typically do not Wnd evidence for the N100 may be that the interstimulus intervals used are too short to accommodate the longer refractory period in younger children. One recently published EEG study on the N100 that used intervals of up to 4393ms in 15 children aged 7±9 years did Wnd evidence supporting the longer refractory hypothesis (Ceponiene et al., 1998). The M100 may be a physiologic index of auditory sensory memory (Lü et al., 1992), and these MEG data may, therefore, imply that the phonologic store duration gets shorter as children mature.

Future research directions

Although there has been some eVort to study AEF development in children, no data have yet been reported concern-

Fig. 5.17. The 100ms latency magnetic Weld refractory period in children. Each data point (mean and SEM) represents average amplitude for both left and right hemispheres taken from the anterior magnetic extremum of the M100 auditory evoked Weld in younger (d, 6±8 years of age) and older (j, 15±17 years of age) children. Data were Wt to a standard exponential decay equation (regression lines), and the time constant of decay from the regression was compared between the two groups. The time constant was signiWcantly longer in the younger subjects, indicating longer neuronal trace duration in younger children. (Data from Rojas et al., 1998.)

ing visual, somatosensory, or motor evoked responses, or responses in any other neurologic domain in younger subjects. Such normative developmental data will be critically important before meaningful studies using MEG in clinical developmental research populations are undertaken. Several potentially exciting applications of MEG lie with the study of sensory/cognitive processing disorders, including childhood-onset schizophrenia, attentiondeWcit hyperactivity disorder (ADHD), developmental dyslexia, and other developmental disabilities.

Childhood-onset schizophrenia

To date, the only published MEG data on any psychiatric disorder is on adults with schizophrenia. Reite et al. (1988) were the Wrst to use MEG in schizophrenia, reporting that the normal interhemispheric axial plane asymmetry of the M50 AEF (analog of the electric P50) generators was not found in six males with schizophrenia, but was found in six nonpsychiatric control subjects. This report was followed by a study of M100 asymmetry in six schizophrenic males and six nonpsychiatric controls in which this atypical symmetry in the schizophrenic patients was also present (Reite et al., 1989). A subsequent study with a new sample including both men and women has revealed a prominent sex diVerence in the expression

92D. C. Rojas, P. D. Teale and M. L. Reite

of this anomalous functional asymmetry, with ten male patients showing a lack of M100 asymmetry and ten female patients showing either normal or enhanced asymmetry compared with ageand sex-matched control subjects (Reite et al., 1997).

Hemispheric anatomic asymmetry in the supratemporal auditory areas is typically present at birth (Witelson and Pallie, 1973), but the extent of functional laterality is presently unknown. Critically, one study of 21 schizophrenic adults and 24 controls has shown that the functional asymmetry of the M100 response in adults is independent of the underlying gross anatomy in both groups (Rojas et al., 1997). The development of functional lateralization of the auditory cortex could be studied in normally developing children and in those with early onset of psychotic symptoms. Such an application of MEG would be a natural extension of the earlier work in adults.

Dyslexia

Two MEG studies have been published on the topic of dyslexia (Salmelin et al., 1996; Vanni et al., 1997). Salmelin et al. (1996) in a study of six adult subjects with developmental dyslexia and eight controls found that visually presented words failed to elicit the normal electrophysiologic response from the left inferior temporo-occipital region in the dyslexic subjects. The normal word-speciWc response, which has also been identiWed by intracranial recording at approximately 200ms poststimulus (Nobre et al., 1994), was absent or signiWcantly delayed in all six dyslexic subjects, suggesting a role for this region in the pathophysiology of dyslexia. In the second study, Vanni et al. (1997) reported that visual motion does activate area V5/MT of the visual cortex in dyslexic adults, superWcially contradicting two previous Wndings from the fMRI literature (Eden et al., 1996; Demb et al., 1997). The diVerence between the MEG and fMRI results raises some important methodological issues that illustrate the potential advantage of combining the two techniques. In the Salmelin et al. study (1996), for example, the MEG responses were delayed in the dyslexics, and fMRI would not be sensitive to this delay because of its coarser temporal resolution. Moreover, while the stimuli used in these experiments were somewhat diVerent, a combined fMRI and MEG study of visual motion addressing the correlation of electrophysiology to blood Xow in dyslexia might best resolve the apparently discrepant Wndings; it is possible, albeit speculative, that reduced blood Xow to V5/MT could cause the delayed latency of the MEG responses (or vice versa). Children with dyslexia have not yet been studied in this manner (either with MEG alone or MEG in combination with other imaging modalities).

Pediatric clinical applications

Pediatric epilepsy

The most common application of MEG to pediatric populations has been in neurology, for source localization of epileptic foci. Much research on MEG in source localization of epileptic spikes has been done in adults (see review by Lewine and Orrison, 1995d), but several papers have been published with a special focus on childhood epilepsy (Paetau et al., 1994; Minami et al., 1995, 1996; Zupanc, 1997; Kamada et al., 1998). Of special note is the interest in the Landau±KleVner syndrome (LKS), a childhood disorder characterized by epileptiform spike and wave discharges and progressive loss of previously acquired language function (manifesting most commonly as verbal auditory agnosia). Paetau et al. (1991) reported localization of spikes in one patient with LKS to the left perisylvian auditory cortex, and a follow-up study of seven patients aged 5±12 years revealed that acoustic stimuli evoked epileptiform discharges from the auditory cortex (Paetau, 1994). Interestingly, although the sounds triggered spikes from the patients with LKS, the AEFs to the same sounds were absent or abnormal in all the subjects. In some cases, the sounds triggered spikes peaking at approximately 100 ms poststimulus, the same latency range for the typical M100 (see above). The sound-evoked spikes were 10±15 times the amplitude of the normal M100, and the authors suggested that the pathophysiology of the LKS spikes might closely correspond with the neuronal population responsible for the M100 (ie., that the seizure activity may represent the disinhibition of normal auditory processing).

Conclusions and future directions

Aside from the small number of clinical applications of MEG currently in place, several of the research directions discussed could lead to the development of important diagnostic capabilities for this imaging strategy. For example, the ability to perform intrauterine electrophysiologic evaluations of fetal brain development might lead to important early diagnostic capabilities with respect to sensorineural hearing impairments and developmental disabilities, as well as a number of other neurologic conditions. Before such applications are possible, more basic research is needed on MEG in clinical, as well as normal, samples, particularly pediatric groups.

MEG instrumentation and data analysis have become increasingly sophisticated since the early 1980s, but the high cost to researchers and clinicians has prevented

extensive application to pediatric, as well as adult, clinical populations. As clinical applications for MEG become more widely accepted within the medical community, more instruments are likely to be ordered/installed, and this will certainly have a beneWcial eVect on system prices. Meanwhile, currently operational research groups have published some promising initial results using MEG in combination with anatomic MRI. As with other functional neuroimaging technologies, MEG is likely to beneWt from combined use with multiple imaging modalities, particularly the widely popular fMRI, where the advantages and disadvantages of the two technologies are complementary. To the best of our knowledge, however, at the date of this writing there are only 33 whole-head MEG systems installed worldwide (six in the USA), as well as an unknown, but most likely lower, number of operational partial array systems. This small number of MEG systems has severely limited the ability of the MEG community to collaborate with their other neuroimaging colleagues. As the installed instrument base of MEG increases, collaborative eVorts such as these will become more likely, and the future of electrophysiological imaging in pediatric psychiatry will get correspondingly brighter.

Acknowledgements

Funding for this work was provided by USPHS grants MH56601, MH47476, MH15442, and HD35468, and by the Developmental Psychobiology Research Group Endowment Fund at the University of Colorado Health Sciences Center, provided by the Grant Foundation. The authors thank Jeanelle L. Sheeder and Jennifer B. Lopez for their assistance with data collection and analysis related to this manuscript.

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